code: def tree_feature_transform(stumps, X):
      """
      Transform the data matrix X using a mapping derived from a collection of
      local decision stump functions.
  
      If the list of stumps is empty, return an array of shape (0, n_samples).
  
      Parameters
      ----------
      stumps: list of LocalDecisionStump objects
          List of stump functions to use to transform data
      X: array-like of shape (n_samples, n_features)
          Original data matrix
  
      Returns
      -------
      X_transformed: array-like of shape (n_samples, n_stumps)
          Transformed data matrix
      """
      transformed_feature_vectors = [np.empty((X.shape[0], 0))]
      for stump in stumps:
          transformed_feature_vec = stump(X)[:, np.newaxis]
          transformed_feature_vectors.append(transformed_feature_vec)
      X_transformed = np.hstack(transformed_feature_vectors)
  
      return X_transformed
  
file_path: imodels/imodels/importance/local_stumps.py
imports: import numpy
label_desc: Use image node_id 3 for calling a global function with example usage: tree_feature_transform(stumps, X) and returns: X_transformed